1. Field of the Invention
This application relates generally to processing three-dimensional or volumetric medical images to improve the detection and diagnosis of disease. More particularly, this application relates to interpolating spatially transformed volumetric medical image data.
2. Description of the Related Art
Medical imaging examinations provide detailed information useful for differentiating, diagnosing, or monitoring the condition, structure, and/or extent of various types of tissue within a patient's body. In general, medical imaging examinations detect and record manners in which tissues respond in the presence of applied signals and/or injected or ingested substances, and generate visual representations indicative of such responses. For example, a contrast agent administered to the patient can selectively enhance or affect the imaging properties of particular tissue types to facilitate improved tissue differentiation. Magnetic resonance imaging (MRI) can excel at distinguishing between malignant and/or benign tumors or lesions that are contrast enhanced relative to healthy tissue in the presence of contrast agent.
Many medical imaging examinations now generate three-dimensional (i.e., volumetric) or four-dimensional (e.g., volumes across time) medical image data. In a clinical setting, the clinician requires computer systems with image analysis solutions that aid in the interpretation of the data. While such systems must be computationally efficient, processing efficiency should not come at the expense of quality, considering that successful disease detection or diagnosis often requires examination of tissue properties (e.g., shape, texture, signal intensity differences) at the pixel or subpixel level.
There are some interpretation tasks, such as those involving spatial transformations of volumetric medical image data, which facilitate a need to estimate missing or needed tissue information. In the field of biomedical image analysis, estimating missing tissue pixel information from existing tissue pixel information is called interpolation. Interpolation of three- and four-dimensional medical image data can be particularly challenging due to the size and non-uniformity of the datasets. Algorithmic efficiency and image deterioration are two factors to consider when selecting an appropriate interpolation procedure. While in the prior art, one factor is typically chosen at the expense of the other factor, it is desirable to provide systems and methods that facilitate interpolation processing both efficiently and with minimal image deterioration.